pcrplot {ENmix} | R Documentation

## Principal component regression plot

### Description

First, principal component analysis will be performed in the standadized input data matrix (standadized for each row/CpG), and then the specified number of top principal components (which explain most data variation) will be used to perform linear regression with each specified variable. Regression P values will be plotted for exploration of methylation data variance structure or identification of possible confounding variables in association analysis.

### Usage

    pcrplot(beta, cov,npc=50,subset=TRUE,subsetsize=50000)

### Arguments

`beta` | A methylation beta value matrix with rows for probes and columns for samples. The input matrix should not contain any missing value.
`cov` | A data frame of covariates. Categorical variables should be converted to factors. The number of rows should equal to the number of columns in beta matrix
`npc` | The number of top ranked principal components to be plotted
`subset` | If TRUE, a random subset probes will be used for the calculation
`subsetsize` | The size of a subset probes

### Value

A jpeg figure "svdscreeplot.jpg" to show the variations explained by each principal component.

A jpeg figure "pcr_diag.jpg" to show association strength between principal components and covariates with cell colors indicating different levels of association P values.

### Author(s)

Zongli Xu

### References

Zongli Xu, Liang Niu, Leping Li and Jack A. Taylor, ENmix: a novel background correction method for Illumina HumanMethylation450 BeadChip. Nucleic Acids Research 2015

### Examples

    if (require(minfiData)) {
    mdat <- preprocessRaw(RGsetEx)
    beta=getBeta(mdat, "Illumina")
    group=pData(mdat)$Sample_Group
    slide=factor(pData(mdat)$Slide)
    cov=data.frame(group,slide)
    pcrplot(beta,cov,npc=6)
    }

[Package ENmix version 1.30.03 Index]